Revealing and Responding to Multiple Health Risks in Informal Settlements in Sub-Saharan African Cities View Full Text


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Article Info

DATE

2019-02

AUTHORS

David Satterthwaite, Alice Sverdlik, Donald Brown

ABSTRACT

This paper underscores the need for detailed data on health and disaster risks for sub-Saharan African cities, particularly for their informal settlements. Systems that should contribute to the information base on health and health risks in each locality are rarely functional. In most cities, there is a lack of data on health risks, health outcomes, and health determinants; where data are available, they are usually too aggregated to be useful to urban governments. Such data shortfalls likely hide the scale of premature death, serious illness, and injury in informal settlements; limited data can also curtail the identification of particularly vulnerable urban residents. After outlining data shortfalls, this paper considers two sources of data that can help fill data gaps on health and health determinants. The first is from city case studies undertaken within a research programme called Urban Africa: Risk Knowledge (Urban-ARK). Urban-ARK's findings reveal the large spectrum of health risks in informal settlements, ranging from 'everyday' risks (e.g. infectious and parasitic diseases) to small- and larger-scale disasters. The second is from data collected by slum/shack dweller federations, which offer qualitative and quantitative findings on health, disasters, and other health determinants in informal settlements. Our conclusion reflects upon the need for additional data on multiple risks to advance urban health and well-being and support the 2030 Agenda for Sustainable Development. It also highlights the need to strengthen accountable urban governance in sub-Saharan Africa. More... »

PAGES

112-122

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11524-018-0264-4

DOI

http://dx.doi.org/10.1007/s11524-018-0264-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104397610

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29873038


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RDF/XML is a standard XML format for linked data.

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